Research Landscape. Qualitative = Constructivist approach. Quantitative = Positivist/post-positivist approach Mixed methods = Pragmatist approach

Transcription

1 Empirical Methods

2 Research Landscape Qualitative = Constructivist approach Build theory from data Quantitative = Positivist/post-positivist approach Mixed methods = Pragmatist approach

3 Experimental Analysis of Mode Switching Techniques in Pen-based User Interfaces Given limited input device, need to overload via modes Studied five different options: Non-preferred Barrel button Pressure Hold Eraser

4 Task

5 Task (2) Each participant did 1800 pie cutting tasks for $25 5 techniques X 9 blocks X 8 orientations X 5 cuts per screen 5-factor repeated measures design, with everything else designed to eliminate confounds Counterbalanced using a 5X5 Latin Square

6 Latin Square Design

7 Findings Time Error

8 Satisfaction

9 Synopses From William, Eddie, Val, Agnes, Hala, Yuexing Please post early! Also: Shorter is fine, but include some thoughts of your own

10 Comments? Hala Seemed like the paper was primarily about pressure-based mode switching Follow on to Ramos s work on pressure widgets. Other comments?

11 Questions for Project Definition How might one do a replication study on this paper? Note: This is a trick question I have done one with Jaime Ruiz and Bill Cowan Lead to 3 published research papers with Jaime and a model of non-preferred mode switching

12 Replication Study

13 Replication Study 2: Bitouch and bipad

14 Overview Wikipedia Any research which bases its findings on observations as a test of reality Accumulation of evidence results from planned research design Academic rigor determines legitimacy Frequently refers to scientific-style experimentation Many qualitative researchers also use this term

15 Positivism Describe only what we can measure/observe No ability to have knowledge beyond that Example: psychology Concentrate only on factors that influence behaviour Do not consider what a person is thinking Assumption is that things are deterministic

16 Post-Positivism A recognition that the scientific method can only answer question in a certain way Often called critical realism There exists objective reality, but we are limited in our ability to study it I am often influenced by my physics background when I talk about this We live in a closed, limited, 4-D universe of a specific size OK, so what s outside the boundary of our universe?

17 Implications of Post-Positivism The idea that all theory is fallible and subject to revision The goal of a scientist should be to disprove something they believe The idea of triangulation Different measures and observations tell you different things, and you need to look across these measures to see what s really going on The idea that biases can creep into any observation that you make, either on your end or on the subject s end

18 Experimental Biases in the RW Hawthorne effect/john Henry effect Experimenter effect/observer-expectancy effect Pygmalion effect Placebo effect Novelty effect

19 Hawthorne Effect Named after the Hawthorne Works factory in Chicago Original experiment asked whether lighting changes would improve productivity Found that anything they did improved productivity, even changing the variable back to the original level. Benefits stopped or studying stopped, the productivity increase went away Why? Motivational effect of interest being shown in them Also, the flip side, the John Henry effect Realization that you are in control group makes you work harder

20 Experimenter Effect A researcher s bias influences what they see Example from Wikipedia: music backmasking Once the subliminal lyrics are pointed out, they become obvious Dowsing Not more likely than chance The issue: If you expect to see something, maybe something in that expectation leads you to see it Solved via double-blind studies

21 Pygmalion effect Self-fulfilling prophecy If you place greater expectation on people, then they tend to perform better Studied teachers and found that they can double the amount of student progress in a year if they believe students are capable If you think someone will excel at a task, then they may, because of your expectation

22 Placebo Effect Subject expectancy If you think the treatment, condition, etc has some benefit, then it may Placebo-based anti-depressants, muscle relaxants, etc. In computing, an improved GUI, a better device, etc. Steve Jobs: Bill Buxton:

23 Novelty Effect Typically with technology Performance improves when technology is instituted because people have increased interest in new technology Examples: Computer-Assisted instruction in secondary schools, computers in the classroom in general, etc.

24 What can you test? Three things? Comparisons Models Exploratory analysis Reading was comparative with some nod to model validation

25 Concepts Randomization and control within an experiment Random assignment of cases to comparison groups Control of the implementation of a manipulated treatment variable Measurement of the outcome with relevant, reliable instruments Internal validity Did the experimental treatments make the difference in this case? Threats to validity History threats (uncontrolled, extraneous events) Instrumentation threats (failure to randomize interviewers/raters across comparison groups Selection threat (when groups are self-selected)

26 Themes HCI context Scott MacKenzie s tutorial Observe and measure Research questions User studies group participation User studies terminology User studies step by step summary Parts of a research paper

27 Observations and Measures Observations Manual (human observer) Using log sheets, notebooks, questionnaires, etc. Automatically Sensors, software, etc. Measurements (numerical) Nominal: Arbitrary assignment of value (1=male, 2=female Ordinal: Rank (e.g. 1 st, 2 nd, 3 rd, etc. Interval: Equal distance between values, but no absolute zero Ratio: Absolute zero, so ratios are meaningful (e.g. 40 wpm is twice as fast as 20 wpm typing) Given measurements and observations, we: Describe, compare, infer, relate, predict

28 Research Questions You have something to test ( a new technique) Untestable questions: Is the technique any good? What are the technique s strengths and weaknesses? Performance limits? How much practice is needed to learn? Testable questions seem narrower See example at right Scott MacKenzie s course notes

29 Research Questions (2) Internal validity Differences (in means) should be a result of experimental factors (e.g. what we are testing) Variances in means result from differences in participants Other variances are controlled or exist randomly External validity Extent to which results can be generalized to broader context Participants in your study are representative Test conditions can be generalized to real world These two can work against each other Problems with Usable

30 Example: Validity trade-off

31 Research Questions (3) Given a testable question (e.g. a new technique is faster) and an experimental design with appropriate internal and external validity You collect data (measurements and observations) Questions: Is there a difference Is the difference large or small Is the difference statistically significant Does the difference matter

32 Significance Testing R. A. Fisher ( ) Considered designer of modern statistical testing Fisher s writings on Decision Theory versus Statistical Inference: An important difference is that Decisions are final while the state of opinion derived from a test of significance is provisional, and capable, not only of confirmation but also of revision (p.100). A test of significance... is intended to aid the process of learning by observational experience. In what it has to teach each case is unique, though we may judge that our information needs supplementing by further observations of the same, or of a different kind (pp ). Implications? What is the difference between statistical testing and qualitative research?

33 Testing Various tests t- and z-tests for two groups ANOVA and variants for multiple groups Regression analysis for modeling Also Binomial test for distributions CHI-Square test for tabular values Great on-line resources:

34 Research Design Participants Formerly subjects Use appropriate number (e.g. similar to what others have used) Independent variable What you manipulate, and what levels of iv were tested (test conditions) Confounding variables Variables that can cause variation Practice, prior knowledge

35 Research Design (2) Within subjects versus between subjects Within = repeated measures Sometimes a choice: Controls subject variances (easier stat significance), but can have interference Counterbalancing Typing on qwerty versus numeric keyboard Could learn phrases, some phrases could be easier, so vary order of devices Latin square

36 Reading Experimental Results Sometimes you need to read carefully to fully appreciate what data is saying Consider Wedge reading